Boundary mass in MoE is linear in slab width under smoothness and transversality, so the zero-temperature limit is governed by a thin geometric layer around routing interfaces rather than the full input space.
Jordan and Robert A
4 Pith papers cite this work. Polarity classification is still indexing.
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Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
A new MoE training method integrates expert-level losses and partial online updates to improve forecasting accuracy and efficiency over standard statistical and neural models.
DeepSeekMoE 2B matches GShard 2.9B performance and approaches a dense 2B model; the 16B version matches LLaMA2-7B at 40% compute by using fine-grained expert segmentation plus shared experts.
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